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CLC number: TP387

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Received: 2004-03-27

Revision Accepted: 2004-12-01

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.387~392


Clustering-based selective neural network ensemble

Author(s):  FU Qiang, HU Shang-xu, ZHAO Sheng-ying

Affiliation(s):  Laboratory of Intelligence Information Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   fuqiang@zju.edu.cn

Key Words:  Neural network, Ensemble, Clustering

FU Qiang, HU Shang-xu, ZHAO Sheng-ying. Clustering-based selective neural network ensemble[J]. Journal of Zhejiang University Science A, 2005, 6(5): 387~392.

@article{title="Clustering-based selective neural network ensemble",
author="FU Qiang, HU Shang-xu, ZHAO Sheng-ying",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Clustering-based selective neural network ensemble
%A FU Qiang
%A HU Shang-xu
%A ZHAO Sheng-ying
%J Journal of Zhejiang University SCIENCE A
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%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0387

T1 - Clustering-based selective neural network ensemble
A1 - FU Qiang
A1 - HU Shang-xu
A1 - ZHAO Sheng-ying
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 5
SP - 387
EP - 392
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0387

An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller ensemble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-off to reduce the prediction error (the sum of bias2 and variance).

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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